class_docstr: Recurrent Experience Replay in Distrib. Reinforcement Learning (R2D2).
  
  Algorithm defining the distributed R2D2 algorithm.
  See `r2d2_[tf|torch]_policy.py` for the definition of the policies.
  
  [1] Recurrent Experience Replay in Distributed Reinforcement Learning -
      S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney - 2019, DeepMind
  
  
  Detailed documentation:
  https://docs.ray.io/en/master/rllib-algorithms.html#    recurrent-replay-distributed-dqn-r2d2
class_docstr_tok: ["Recurrent","Experience","Replay","in","Distrib",".","Reinforcement","Learning","(","R2D2",")",".","Algorithm","defining","the","distributed","R2D2","algorithm",".","See","`","r2d2_","[","tf|torch","]","_policy.py","`","for","the","definition","of","the","policies",".","[","1","]","Recurrent","Experience","Replay","in","Distributed","Reinforcement","Learning","-","S","Kapturowski",",","G","Ostrovski",",","J","Quan",",","R","Munos",",","W","Dabney","-","2019",",","DeepMind","Detailed","documentation",":","https",":","\/\/docs.ray.io\/en\/master\/rllib-algorithms.html","#","recurrent-replay-distributed-dqn-r2d2"]
code: def get_default_policy_class(
      cls, config: AlgorithmConfig
  ) -> Optional[Type[Policy]]:
      if config["framework"] == "torch":
          return R2D2TorchPolicy
      else:
          return R2D2TFPolicy
  
file_path: ray/rllib_contrib/r2d2/src/rllib_r2d2/r2d2/r2d2.py
imports: import logging
  from typing import Optional, Type
  from rllib_r2d2.r2d2.r2d2_tf_policy import R2D2TFPolicy
  from rllib_r2d2.r2d2.r2d2_torch_policy import R2D2TorchPolicy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dqn import DQN, DQNConfig
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
label_desc: Use image node_id 2 for calling the R2D2 obj's underlying member method code with example usage: obj.get_default_policy_class(cls, config) and returns: R2D2TorchPolicy, R2D2TFPolicy